In the field of optical pattern recognition, there is a need for simple and reliable devices that recognize specified optical patterns. Such devices are useful, for example, in industrial robots for distinguishing different physical objects, in photo-interpretation for automatically scanning for specific images, and in missile guidance systems for real-time target acquisition. In military and space applications, real-time image recognition is often needed in environments where a human observer cannot be present. In such remote environments, it is usually desirable that an image recognition system be small, light weight, and reliable.
For effective operation, optical pattern recognition systems generally require proper alignment of an input image with respect to a stored reference. Variations in orientation or size of the input image when compared to the on-board reference can lead to non-recognition of the image. Therefore, the adaptability of a system to variations in an input scene is a factor that can determine whether or not the pattern recognition process is successful. In dynamic or uncertain environments, correlation of input images that deviate in orientation or scale from a given reference continues to be a problem in pattern recognition systems.
Prior methods of pattern recognition designed to be invariant with transformations such as size and rotation have met with limited success. These prior methods range from the systematic extraction of pattern features to the common but inefficient technique of storing multiple rotated and scaled versions of the patterns to be recognized. The latter technique is useful in some situations, but it requires the storage of a vast quantity of necessarily redundant data. Thus, a need remains for an optical pattern recognition system that can perform reliable image correlations without the prior storage of a large library of redundant images.